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AGI will be embedded into humanoid robots, which makes white-collar and blue-collar jobs a target ... More for walking/talking automation.getty In today’s column, I examine the highly worrisome qualms expressed that the advent of artificial general intelligence (AGI) is likely to usurp white-collar jobs. The stated concern is that since AGI will be on par with human intellect, any job that relies principally on intellectual pursuits such as typical white-collar work will be taken over via the use of AGI. Employers will realize that rather than dealing with human white-collar workers, they can more readily get the job done via AGI. This, in turn, has led to a rising call that people should aim toward blue-collar jobs, doing so because (presumably) those forms of employment will not be undercut via AGI. Sorry to say, that misses the bigger picture, namely that AGI when combined with humanoid robots is coming not only for white-collar jobs but also blue-collar jobs too. It is a proverbial double-whammy when it comes to the attainment of AGI. Let’s talk about it. This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). Heading Toward AGI And ASI First, some fundamentals are required to set the stage for this weighty discussion. There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI). AGI is AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many if not all feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here. We have not yet attained AGI. In fact, it is unknown as to whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI. AGI Problem Only Half Seen Before launching into the primary matter at hand in this discussion, let’s contemplate a famous quote attributed to Charles Kettering, a legendary inventor, who said, “A problem well-stated is a problem half-solved.” I bring this up because those loud clamors right now about the assumption that AGI will replace white-collar workers are only seeing half of the problem. The problem as they see it is that since AGI is intellectually on par with humans, and since white-collar workers mainly use intellect in their work endeavors, AGI is going to be used in place of humans for white-collar work. I will in a moment explain why that’s only half of the problem and there is a demonstrative need to more carefully and fully articulate the nature of the problem. Will AGI Axiomatically Take White-Collar Jobs On a related facet, the belief that AGI will axiomatically replace white-collar labor makes a number of other related key assumptions. I shall briefly explore those and then come back to why the problem itself is only half-baked. The cost of using AGI for doing white-collar work will need to be presumably a better ROI choice over human workers. If not, then an employer would be wiser to stick with humans rather than employing AGI. There seems to often be an unstated belief that AGI is necessarily going to be a less costly route than employing humans. We don’t know yet what the cost of using AGI will be. It could be highly expensive. Indeed, some are worried that the world will divide into the AGI haves and AGI have-nots, partially due to the exorbitant cost that AGI might involve. If AGI is free to use, well, that would seem to be the nail in the coffin related to using human workers for the same capacity. Another angle is that AGI is relatively inexpensive in comparison to human labor. In that case, the use of AGI is likely to win over human labor usage. But if the cost of AGI is nearer to the cost of human labor (all in), or more so, then employers would rationally need to weigh the use of one versus the other. Note that when referring to the cost of human labor, there is more to that calculation than simply the dollar-hour labor rate per se. There are lots of other less apparent costs, such as the cost to manage human labor, the cost of dealing with HR-related issues, and many other factors that come into the weighty matter. Thus, an AGI versus human labor ROI will be more complex than it might seem at an initial glance. In addition, keep in mind that AGI would seemingly be readily switched on and off, and have other capacities that human labor would not equally tend to allow. The Other Half Is Coming Too Assume that by and large the advent of AGI will decimate the need for white-collar human labor. The refrain right now is that people should begin tilting toward blue-collar jobs as an alternative to white-collar jobs. This is a logical form of thinking in the sense that AGI as an intellectual mechanism would be unable to compete in jobs that involve hands-on work. A plumber needs to come to your house and do hands-on work to fix your plumbing. This is a physicality that entails arriving at your physical home, physically bringing and using tools, and physically repairing your faulty home plumbing. A truck driver likewise needs to sit in the cab of a truck and drive the vehicle. These are physically based tasks. There is no getting around the fact that these are hands-on activities. Aha, yes, those are physical tasks, but that doesn’t necessarily mean that only human hands can perform them. The gradual emergence of humanoid robots will provide an alternative to human hands. A humanoid robot is a type of robot that is built to resemble a human in form and function. You’ve undoubtedly seen those types of robots in the many online video recordings showing them walking, jumping, grasping at objects, and so on. A tremendous amount of active research and development is taking place to devise humanoid robots. They look comical right now. You watch those videos and laugh when the robot trips over a mere stick lying on the ground, something that a human would seldom trip over. You scoff when a robot tries to grasp a coffee cup and inadvertently spills most of the coffee. It all seems humorous and a silly pursuit. Keep in mind that we are all observing the development process while it is still taking place. At some point, those guffaws of the humanoid robots will lessen. Humanoid robots will be as smooth and graceful as humans. This will continue to be honed. Eventually, humanoid robots will be less prone to physical errors that humans make. In a sense, the physicality of a humanoid robot will be on par with humans, if not better, due to its mechanical properties. Do not discount the coming era of quite physically capable humanoid robots. AGI And Humanoid Robots Pair Up You might remember that in The Wonderful Wizard of Oz, the fictional character known as The Strawman lacked a brain. Without seeming to anthropomorphize humanoid robots, the current situation is that those robots typically use a form of AI that is below the sophistication level of modern generative AI. That’s fine for now due to the need to first ensure that the physical movements of the robots get refined. I have discussed that a said-to-be realm of Physical AI is going to be a huge breakthrough with incredible ramifications, see my analysis at the link here. The idea underlying Physical AI is that the AI of today is being uplifted by doing data training on the physical world. This also tends to include the use of World Models, consisting of broad constructions about how the physical world works, such as that we are bound to operate under conditions of gravity, and other physical laws of nature, see the link here. The bottom line here is that there will be a close pairing of robust AI with humanoid robots. Imagine what a humanoid robot can accomplish if it is paired with AGI. I’ll break the suspense and point out that AGI paired with humanoid robots means that those robots readily enter the blue-collar worker realm. Suppose your plumbing needs fixing. No worries, a humanoid robot that encompasses AGI will be sent to your home. The AGI is astute enough to carry on conversations with you, and the AGI also fully operates the robot to undertake the plumbing tasks. How did the AGI-paired humanoid robot get to your home? Easy-peasy, it drove a car or truck to get there. I’ve previously predicted that all the work on devising autonomous vehicles and self-driving cars will get shaken up once we have suitable humanoid robots devised. There won’t be a need for a vehicle to contain self-driving capabilities. A humanoid robot will simply sit in the driver’s seat and drive the vehicle. This is a much more open-ended solution than having to craft components that go into and onto a vehicle to enable self-driving. See my coverage at the link here. Timing Is Notable One of the reasons that many do not give much thought to the pairing of AGI with humanoid robots is that today’s humanoid robots seem extraordinarily rudimentary and incapable of performing physical dexterity tasks on par with human capabilities. Meanwhile, there is brazen talk that AGI is just around the corner. AGI is said to be within our grasp. Let’s give the timing considerations a bit of scrutiny. There are three primary timing angles: Option 1: AGI first, then humanoid robots. AGI is attained before humanoid robots are sufficiently devised. Option 2: Humanoid robots first, then AGI. Humanoid robots are physically fluently adept before AGI is attained. Option 3: AGI and humanoid robots arrive about at the same time. AGI is attained and at the same time, it turns out that humanoid robots are fluently adept too, mainly by coincidence and not due to any cross-mixing. A skeptic would insist that there is a fourth possibility, consisting of the possibility that we never achieve AGI and/or we fail to achieve sufficiently physically capable humanoid robots. I am going to reject that possibility. Perhaps I am overly optimistic, but it seems to me that we will eventually attain AGI, and we will eventually attain physically capable humanoid robots. I shall next respectively consider each of the three genuinely reasonable possibilities. Option 1: AGI First, Then Humanoid Robots What if we manage to attain AGI before we manage to achieve physically fluent humanoid robots? That’s just fine. We would indubitably put AGI to work as a partner with humans in figuring out how we can push along the budding humanoid robot development process. It seems nearly obvious that with AGI’s capable assistance, we would overcome any bottlenecks and soon enough arrive at top-notch physically adept humanoid robots. At that juncture, we would then toss AGI into the humanoid robots and have ourselves quite an amazing combination. Option 2: Humanoid Robots First, Then AGI Suppose that we devise very physically adept humanoid robots but have not yet arrived at AGI. Are we in a pickle? Nope. We could use conventional advanced AI inside those humanoid robots. The combination would certainly be good enough for a wide variety of tasks. The odds are that we would need to be cautious about where such robots are utilized. Nonetheless, we would have essentially walking, talking, and productive humanoid robots. If AGI never happens, oh well, we end up with pretty good humanoid robots. On the other hand, once we arrive at AGI, those humanoid robots will be stellar. It’s just a matter of time. Option 3: AGI And Humanoid Robots At The Same Time Let’s consider the potential of AGI and humanoid robots perchance being attained around the same time. Assume that this timing isn’t due to an outright cross-mixing with each other. They just so happen to advance on a similar timeline. I tend to believe that’s the most likely of the three scenarios. Here’s why. First, despite all the hubris about AGI being within earshot, perhaps in the next year or two, which is a popular pronouncement by many AI luminaries, I tend to side with recent surveys of AI developers that put the date around the year 2040 (see my coverage at the link here). Some AI luminaires sneakily play with the definition of AGI in hopes of making their predictions come true sooner, akin to moving the goalposts to easily score points. For my coverage on Sam Altman’s efforts of moving the cheese regarding AGI attainment, see the link here. Second, if you are willing to entertain the year 2040 as a potential date for achieving AGI, that’s about 15 years from now. In my estimation, the advancements being made in humanoid robots will readily progress such that by 2040 they will be very physically adept. Probably be sooner, but let’s go with the year 2040 for ease of contemplation. In my view, we will likely have humanoid robots doing well enough that they will be put into use prior to arriving at AGI. The pinnacle of robust humanoid robots and the attainment of AGI will roughly coincide with each other. Two peas in a pod.Impact Of Enormous Consequences In an upcoming column posting, I will examine the enormous consequences of having AGI paired with fully physically capable humanoid robots. As noted above, this will have a humongous impact on white-collar work and blue-collar work. There will be gargantuan economic impacts, societal impacts, cultural impacts, and so on. Some final thoughts for now. A single whammy is already being hotly debated. The debates currently tend to be preoccupied with the loss of white-collar jobs due to the attainment of AGI. A saving grace seems to be that at least blue-collar jobs are going to be around and thriving, even once AGI is attained. The world doesn’t seem overly gloomy if you can cling to the upbeat posture that blue-collar tasks remain intact. The double whammy is a lot more to take in. But the double whammy is the truth. The truth needs to be faced. If you are having doubts as a human about the future, just remember the famous words of Vince Lombardi: “Winners never quit, and quitters never win.” Humankind can handle the double whammy. Stay tuned for my upcoming coverage of what this entails.

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From lifelike avatars to automated fan interactions, AI is remaking digital modeling. But can tech ... More scale intimacy — or will it erode the human spark behind the screen?getty The AI boom has been defined by unprecedented innovation across nearly every sector. From improving flight punctuality through AI-powered scheduling to detecting early markers of Alzheimer’s disease, AI is modifying how we live and work. And the advertising world isn’t left out. In March of this year, OpenAI’s GPT-4o sent the internet into a frenzy with its ability to generate Studio Ghibli-style images. The model produces realistic, emotionally nuanced visuals from a series of prompts — a feat that has led some to predict the demise of visual arts as we know them. While such conclusions may be premature, there’s growing belief among industry players that AI could transform how digital model agencies operate. That belief isn’t limited to one startup. A new class of AI-powered agencies — including FanPro, Lalaland.ai, Deep Agency andThe Diigitals — is testing whether modeling can be automated without losing its creative edge. Some use AI to generate lifelike avatars. Others offer virtual photo studios, CRM — customer relationship management — integrations, or creator monetization tools. Together, they reflect a big shift in how digital modeling agencies think about labor, revenue and scale. FanPro — founded by Tyron Humphris in 2023 to help digital model agencies scale efficiently — offers a striking case study. Fully self-funded, Humphris said in an interview that the company reached $1 million in revenue within its first 90 days and crossed eight figures by 2024, all while maintaining a lean team by automating nearly every process. As Humphris noted, “the companies that will lead this next decade won’t just be the ones with the best marketing or biggest budgets. They’ll be the ones who use AI, automation and systems thinking to scale with precision, all while staying lean and agile.” That explains the big bet that startups like FanPro are making — but how far can it really go? And why should digital model agencies care in the first place? Automation In Digital Model Agencies To understand how automation works in the digital modeling industry — a fast-rising corner of the creator economy — it helps to understand what it’s replacing. A typical digital model agency juggles five or more monetization platforms per creator — from OnlyFans and Fansly to TikTok and Instagram. But behind every viral post is a grind of scheduling, analytics, upselling, customer support and retention. The average agency may need 10 to 15 contractors to manage a roster of just a few high-performing creators. These agencies oversee a complex cycle: content creation, onboarding, audience engagement and sales funnel optimization, usually across several monetization platforms. According to Humphris, there’s often a misconception that running a digital model agency is just about posting pretty pictures. But in reality, he noted, it’s more. “It’s CRM, data science and psychology all wrapped in one. If AI can streamline even half of that, it’s a game-changer.” That claim reflects a growing pain point in the creator economy, where agencies swim in an ocean of tools in an attempt to monetize attention for creators while simultaneously managing marketing, sales and customer support. For context, a 2024 Clevertouch Consulting study revealed that 54% of marketers use more than 50 tools to manage operations — many stitched together with Zapier or manual workarounds. Tyron Humphris, founder of FanProFanPro But, according to Humphris, “no matter how strong your offer is, if you don’t have systems, processes and accountability built into the business, it’s going to collapse under pressure.” And that’s where AI steps in. Beyond handling routine tasks, large language models and automation stacks now allow agencies to scale operations while staying lean. With minimal human input, agencies can schedule posts, auto-respond to DMs, upsell subscriptions, track social analytics and manage retention flows. What once required a full team of marketers, virtual assistants and sales reps can now be executed by a few well-trained AI agents. FanPro claims that over 90% of its operations — from dynamic pricing to fan interactions — are now handled by automation. Likewise, Deep Agency allows creators to generate professional-grade photo shoots without booking a studio or hiring staff and Lalaland.ai helps fashion brands generate AI avatars to reduce production costs and increase diversity in representation. A Necessary Human Touch Still, not everyone is convinced that AI can capture the nuance of digital intimacy. Some experts have raised concerns that hyper automation in creator-driven industries could flatten human expression into predictable engagement patterns, risking long-term user loyalty. A 2024 ContentGrip study of 1,000 consumers found 80% of respondents would likely switch brands that rely heavily on AI-generated emails, citing a loss of authenticity. Nearly half said such messages made them feel “less connected” to the brand. Humphris doesn’t disagree. “AI can do a lot, but it needs to be paired with someone who understands psychology,” he said. “We didn’t scale because we had the best tech. We scaled because we understood human behavior and built systems that respected it.” Humphris’ sentiment isn’t a mere anecdote but one rooted in research. For example, a recent study by Northeastern University showed that AI influencers often reduce brand trust — especially when users aren’t aware the content is AI-generated. The implication is clear: over-automating the wrong parts of human interaction can backfire. Automation doesn’t — and shouldn’t — mean that human input becomes obsolete. Rather, as many industry experts have noted, it will enhance efficiency but not replace empathy. While AI can process data at speed and generate alluring visuals, it cannot replicate human creativity or emotional intelligence. Neither does AI know the psychology of human behavior like humans do, a trait Humphris credits for their almost-instant success. What’s Working — And What’s Not Lalaland.ai and The Diigitals have earned praise for enhancing inclusivity, enabling brands to feature underrepresented body types, skin tones and styles. Meanwhile, FanPro focuses on building AI “growth engines” for agencies — full-stack systems that combine monetization tools, CRM and content flows. But not all reactions have been positive. In November 2024, fashion brand Mango faced backlash for its use of AI-generated models, which critics called “false advertising” and “a threat to real jobs.” The New York Post covered the fallout in detail, highlighting how ethical lines are still being drawn. As brands look to balance cost savings with authenticity, some have begun labeling AI-generated content more clearly — or embedding human oversight into workflows, rather than removing it. Despite offering an automation stack, FanPro itself wasn’t an immediate adopter of automation in its processes. But, as Humphris noted, embracing AI made all the difference for the company. “If we had adopted AI and automation earlier, we would’ve hit 8 figures much faster and with far less stress,” he noted. Automation In The New Era FanPro is a great example of how AI integration, when done the right way, could be a profitable venture for digital model agencies. Whether or not the company’s model becomes the blueprint for AI-first digital agencies, it’s clear that there’s a big shift in the creator economy, where automation isn’t only viewed as a time-saver, but also as a foundational pillar for businesses. As digital model agencies lean further into an AI-centric future, the bigger task is remembering what not to automate — the spark of human connection that built the industry in the first place. “In this new era of automation,” Humphris said, “the smartest agencies won’t just ask what AI can do. They’ll ask what it shouldn’t.”

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Floyd Mayweather and Donald Trump, HBO Boxing Icon says, "No Mayweather, No Trump"Credit: Getty North America - Mayweather (Allen Berezovsky / Contributor) Trump - (Kevin Dietsch / Staff) “No Mayweather, no Trump.” That’s the heavy-duty take HBO Boxing icon Jim Lampley dropped on me during our conversation on Friday afternoon. It’s one of the most polarizing, but plausible things anyone has said to me in a while. In case you’re a relatively young sports fan or someone who didn’t pay much attention to HBO Boxing during the 1990s and early 2000s, you may not know Lampley. Without listing every accolade and distinction, I’ll say this: there are people in every industry who have a combination of experience and wherewithal that enables them to captivate a room with the stories and takes they’ve accumulated during their journey through levels of their craft and the years of their life. Lampley is one of those people.CANASTOTA, NY - JUNE 14: Boxing commentator Jim Lampley poses with his new ring and photo on the ... More wall after the induction ceremony at the International Boxing Hall of Fame induction Weekend of Champions events on June 14, 2015 in Canastota, New York. (Photo by Alex Menendez/Getty Images)Getty Images During our interview, we discussed his book, It Happened, his autobiography that tells the story of his 50-year career in sports broadcasting and tons of boxing stories and concepts. I asked him about the infamous post-fight moment with his longtime broadcast partner Larry Merchant—you know the one, where Mayweather snapped, “you don’t know s### about boxing,” and Merchant fired back, “if I was 50 years younger, I’d kick your a##.” It’s classic. I wanted to know from Lampley, who was there, what the deal was with Mayweather and Merchant, but I got so much more. Lampley explained, “Well, obviously it was something that built up over a long period of time. I think that Larry gave him proper credit for being a great fighter. In particular, he didn't like that Larry would sometimes say something to the effect of, well, he's great and you can't beat him, but he isn't gonna sell a single ticket with this style, that kind of thing.” Clearly, Merchant was wrong about Mayweather’s ability to sell tickets. No fighter has made more in pay-per-view revenue, and Mayweather can still command a crowd and a payday by participating in boxing exhibitions. Also, if you watch the history of post-fight interviews between Mayweather and Merchant, the latter is particularly abrupt and a little antagonistic toward the fighter. Even during the aforementioned interview, the conversation starts off with Mayweather having his arm around Merchant. However, Merchant’s tone and verbiage was off-putting to Mayweather as it came just moments after the controversial ending to his fight with Victor Ortiz, but I digress. That aspect of Lampley’s answer was mostly what I expected to hear. The broadcast journalism legend delivered a punchier concept as he delved deeper into Mayweather’s dynamic and his overall influence on American culture.NEW YORK, NEW YORK - FEBRUARY 26: Floyd Mayweather Jr. visits "Making Money With Charles Payne" at ... More Fox Business Network Studios on February 26, 2025 in New York City. (Photo by John Nacion/Getty Images)Getty Images “One of the things I say, have said over and over and will continue to say until proven otherwise, no Mayweather, no Trump,” Lampley said. Trump is a boxing fan, Trump watched what Floyd did. Trump realized from what Floyd did that you could build and develop a social media audience and you could do it with negativity. You didn’t have to be happy talk. You didn't have to be a great guy. There were some people who would rather that someone was channeling their angst and their frustration and venting it on the web. And I believe to this day, no Mayweather, no Trump.” The concept is wild, but not completely unfathomable. I used to say this about Mayweather, ‘it takes a special kind of person to be at peace with being the most hated person in a room.’ In that sense, it’s easy to see tons of people in every industry who have adopted that approach–whether they got it from Mayweather or someone else.DETROIT, MICHIGAN - OCTOBER 18: Republican presidential nominee, former U.S. President Donald Trump, ... More brings boxing legend Thomas Hearns to the stage during a campaign rally on October 18, 2024, in Detroit, Michigan. There are 17 days remaining until the U.S. presidential election, which will take place on Tuesday, November 5, 2024. (Photo by Win McNamee/Getty Images)Getty Images Donald Trump’s involvement with boxing dates back to the late 1980s and early 1990s, when Trump Plaza in Atlantic City became a key venue for major fights. At its peak, the hotel and casino played host to several heavyweight title bouts, including, Mike Tyson vs. Michael Spinks in 1988 and Evander Holyfield vs. George Foreman in 1991. Trump didn’t just rent out space for the fights—he appeared to actively positioned himself as a central figure in these events, often appearing ringside, hosting press conferences, and getting photographed with the fighters.ATLANTIC CITY, NJ - JANUARY 22: Businessman Donald Trump in ring with boxer Mike Tyson after ... More knocking out opponent Larry Holmes at Tyson vs Holmes Convention Hall in Atlantic City, New Jersey January 22 1988. (Photo by Jeffrey Asher/ Getty Images)Getty Images Some may argue Trump used these moments to build prestige, associate himself with dominance and success, and to thrust himself into the spotlight. Needless to say, it seems to have worked.ATLANTIC CITY, NJ - JANUARY 22: Businessman Donald Trump and Champion Boxer Evander Holyfield at ... More Tyson vs Holmes Convention Hall in Atlantic City, New Jersey January 22 1988. (Photo by Jeffrey Asher/ Getty Images)Getty Images Putting a bow on the Mayweather-Merchant beef: “So Floyd was the original social media genius and the essence of the genius was to prove that you could be negative rather than positive and you would build an audience that way,” Lampley continued. “And so Floyd didn’t like the honest straightforward evaluations that Larry offered on the air and most particularly about his style of fighting that he was a hide-and-seek fighter, that he was not trying to do damage in there, et cetera, et cetera. And I thought that was palpably visible. Floyd just didn’t want anybody to say it. So they didn't like each other and it was really more Floyd disliking Larry than vice versa." Mayweather’s rise to global prominence began in the mid-2000s. Specifically, his bet-on-myself fight against Oscar De La Hoya in 2007. The fight marked Mayweather’s triumphant separation from Top Rank Boxing and it also was the cornerstone moment of his shift from “Pretty Boy” to “Money.” Mayweather and Trump’s paths would more publicly cross years later. In Mayweather may have used social media to push negative concepts in the past. However, to his credit, I interviewed him earlier this year, and he formally apologized for every time he used social media to be toxic. That admission actually adds some credence to Lampley’s concept. I’ve never had an opportunity to speak to President Trump, but I can promise you, if I do, I will ask him if he learned anything from his encounters with Mayweather. Whether Trump truly studied Mayweather’s moves or simply mirrored them instinctively, the similarities are clear—and Lampley might be one of the only people to call it out this directly.

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The new ES90 electric car is the flagship of Volvo's latest digital safety tech.TT News Agency/AFP via Getty Images For decades, the Volvo brand has been synonymous with safety. But keeping passengers secure is no longer just about a strong cabin or cleverly designed crumple zones. Increasingly, safety is about semi-autonomous driving technology that can mitigate collisions or even avoid them entirely. Volvo intends to be ahead of the game in this era too. Its secret weapon? Something called “Gaussian Splatting”. I asked Volvo’s Head of Software Engineering Alwin Bakkenes and subsidiary Zenseact’s VP Product Erik Coelingh exactly what this is and why it’s so important. Volvo: Early Application Of Safety Data “We have a long history of innovations based on data,” says Bakkenes. “The accident research team from the 70s started with measuring tapes. Now in the digital world we’re collecting millions of real-life events. That data has helped us over the years to develop a three-point safety belt and the whiplash protection system. Now, we can see from the data we collect from fleets that a very large portion of serious accidents happen in the dark on country roads where vulnerable road users are involved. That’s why, with the ES90 that we just launched, we are also introducing a function called lighter AES where we have enabled the car to steer away from pedestrians walking on the side of the road or cyclists, which in the dark you can’t see even if you have your high beam on. This technology picks that up earlier than a human driver.” The Volvo EX90 SUV will also benefit from this technology. Volvo Cars uses AI and virtual worlds with the aim to create safer carsVolvo “If you want to lead in collision avoidance and self-driving, you need to have the best possible data from the real world,” adds Bakkenes. “But everyone is looking also at augmenting that with simulated data. The next step is fast automation, so we’re using state-of-the-art end-to-end models to achieve speed in iterations. But sometimes these models hallucinate. To avoid that, we use our 98 years of safety experience and these millions of data points as guardrails to make sure that the car behaves well because we believe that when you start to automate it needs to be trusted. For us every kilometer driven with Pilot Assist or Pilot Assist Plus needs to be safer than when you've driven it yourself. In the world of AI data is king. We use Gaussian Splatting to enhance our data set.” What Is Volvo’s Gaussian Splatting? “Cars are driven all around the world in different weather and traffic conditions by different people,” says Coelingh. “The variation is huge. We collect millions of data points, but it’s still a limited amount compared to reality. Gaussian Splatting is a new technology that some of our PhD students have been developing the last few years into a system where you can take a single data point from the real world where you have all the sensor, camera, radar and LIDAR sequences and then blow it up into thousands or tens of thousands of different scenarios. In that way, you can get a much better representation of the real world because we can test our software against this huge variation. If you do it in software, you can test much faster, so then you can iterate your software much more quickly and improve our product.” “Gaussian Splatting is used in different areas of AI,” continues Coelingh. “It comes from the neural radiance fields (NeRFs).” The original version worked with static images. “The first academic paper was about a drum kit where somebody took still pictures from different angles and then the neural net was trained on those pictures to create a 3D model. It looked perfect from any angle even though there was only a limited set of pictures available. Later that technology was expanded from 3D to 4D space-time, so you could also do it on the video set. We now do this not just with video data, but also with LiDAR and radar data.” A real-world event can be recreated from every angle. “We can start to manipulate other road users in this scenario. We can manipulate real world scenarios and do different simulations around this to make sure that our system is robust to variations.”Gaussian Splatting allows multiple scenario variations to be created from one real event.Volvo Volvo uses this system particularly to explore how small adjustments could prevent accidents. “Most of the work that we do is not about the crash itself,” says Coelingh. “It’s much more about what's happening 4-5 seconds before the crash or potential crash. The data we probe is from crashes, but it's also from events where our systems already did an intervention and in many cases those interventions come in time to prevent an accident and in some cases they come late and we only mitigated it. But all these scenarios are relevant because they happen in the real world, and they are types of edge case. These are rare, but through this technology of Gaussian Splatting, we can go from a few edge cases to suddenly many different edge cases and thereby test our system against those in a way that we previously could not.” Volvo’s Global Safety Focus This is increasingly important for addressing the huge variation in global driving habits and conditions a safety system will be expected to encounter. “Neural Nets are good at learning these types of patterns,” says Coelingh. “Humans can see that because of the behavior of a car the driver is talking into their phone, either slowing down or wiggling in the lane. If you have an end-to-end neural network using representations from camera images, LiDAR and radar, it will anticipate those kinds of things. We are probing data from cars all around the world where Volvo Cars are being driven.” The system acts preemptively, so it can perform a safety maneuver for example when a pedestrian appears suddenly in the path of the vehicle. “You have no time to react,” says Coelingh. Volvo’s safety system will be ready, however. “Even before that, the car already detects free space. It can do an auto steer and it’s a very small correction. It doesn't steer you out of lane. It doesn't jerk you around. It slows down a little bit and it does the correction. It's undramatic, but the impact is massive. Oncoming collisions are incredibly severe. Small adjustments can have big benefits.”Volvo's safety tech can detect pedestrians the human driver may not have seen.Volvo Volvo has developed one software platform to cover both safety and autonomy. “The software stack that we develop is being used in different ways,” says Coelingh. “We want the driver to drive manually undisturbed unless there’s a critical situation. Then we try to assist in the best possible way to avoid collision, either by warning, steering, auto braking or a combination of those. Then we also do cruising or L2 automation.” Volvo demonstrated how it has been using Gaussian Splatting at NVIDIA’s GTC in April. “We went deeply into the safe automation concept,” says Bakkenes. “Neural nets are good at picking up things that you can’t do in a rule-based system. We're developing one stack based on good fleet data which has end-to-end algorithms to achieve massive performance, and it has guard rails to make sure we manage hallucinations. It's not like we have a collision avoidance stack and then we have self-driving stack.” “There was a conscious decision that if we improve performance, then we want the benefits of that to be both for collision avoidance in manual driving and for self-driving,” says Coelingh. “We build everything from the same stack, but the stack itself is scalable. It’s one big neural network that we can train. But then there are parts that we can deploy separately to go from our core premium ADAS system all the way to a system that can do unsupervised automation. Volvo’s purpose is to get to zero collisions, saving lives. We use AI and all our energy to get there.”

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